Resilient Multi-path Routing Based on a Biological Attractor Selection Scheme

Leibnitz, Kenji; Wakamiya, Naoki; Murata, Masayuki

doi:10.1007/11613022_7

Cited by 20 publications

(37 citation statements)

References 18 publications

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“…Leibnitz et al [3]- [5] extended the 2-dimensional attractor selection to the M-dimensional one, and proposed the method that can adaptively select one good state from M states according to the current environment. Since Leibnitz's method inherits adaptability from the original attractor selection, it can re-select a good state adaptively to the environmental changes.…”

Section: Leibnitz's Methodsmentioning

confidence: 99%

“…The behavior of activity α is different in each method in [3], [4] and [5]. Thus, we explain only overview here.…”

Section: Leibnitz's Methodsmentioning

confidence: 99%

“…The original attractor selection models the behavior of E. coli cells that adapt to changes in the amount of available nutrient even if molecular machinery for signal transduction to the DNA is unavailable. Leibnitz et al [3]- [5] extended the original 2-dimensional attractor selection to the M-dimensional one, and applied it to multi-path routing in overlay networks [3], [4] and in ad-hoc networks [5]. Our protocol is also based on Leibnitz's method.…”

Section: Attractor Selectionmentioning

confidence: 99%

“…Leibnitz et al proposed several protocols based on the attractor selection for routing in overlay networks [3], [4] and in mobile ad hoc networks [5]. Leibnitz's method can select a good route adaptively to environments when each station knows the information (e.g.…”

Section: Introductionmentioning

confidence: 99%

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A Self-Adaptive Routing Protocol in Wireless LANs Based on Attractor Selection

Nishikawa

Izumi

Ooshita

et al. 2009

IEICE Trans. Inf. & Syst.

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SUMMARYWireless LANs, which consist of access points and wireless stations, have widely spread in recent years. Routing in wireless LANs suffers the problem that each wireless station selects an access point and a wired path to its destination station. It is desired to design an adaptive routing protocol for wireless LANs since throughputs of communications are dynamically affected by selections of other wireless stations and external environmental changes. In this paper, we propose a routing protocol for wireless LANs based on attractor selection. Attractor selection is a biologically inspired approach, and it has high adaptability to dynamic environmental changes. By applying attractor selection, each wireless station can adaptively select its access point and wired path with high throughput against environmental changes. In addition, we design the protocol with a new technique: combination of multiple attractor selections. The technique is useful because it enables us to divide a problem into several simpler problems. To the best of our knowledge, our protocol is the first one designed around a combination of multiple attractor selections. We show the effectiveness and adaptability of our protocol by simulations.

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Section: Leibnitz's Methodsmentioning

confidence: 99%

“…The behavior of activity α is different in each method in [3], [4] and [5]. Thus, we explain only overview here.…”

Section: Leibnitz's Methodsmentioning

confidence: 99%

Section: Attractor Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Self-Adaptive Routing Protocol in Wireless LANs Based on Attractor Selection

Nishikawa

Izumi

Ooshita

et al. 2009

IEICE Trans. Inf. & Syst.

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“…Noise in nonlinear dynamics is not always obstacle for computation but it can help to realize effective and reliable results if its strength are suitably tuned [7], [33], [34]. Examples include efficient dynamic routing utilizing fluctuation [35], [36].…”

Section: Active Roles Of Fluctuationmentioning

confidence: 99%

Brain-Inspired Communication Technologies: Information Networks with Continuing Internal Dynamics and Fluctuation

Teramae

Wakamiya

2015

IEICE Trans. Commun.

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SUMMARYComputation in the brain is realized in complicated, heterogeneous, and extremely large-scale network of neurons. About a hundred billion neurons communicate with each other by action potentials called "spike firings" that are delivered to thousands of other neurons from each. Repeated integration and networking of these spike trains in the network finally form the substance of our cognition, perception, planning, and motor control. Beyond conventional views of neural network mechanisms, recent rapid advances in both experimental and theoretical neuroscience unveil that the brain is a dynamical system to actively treat environmental information rather passively process it. The brain utilizes internal dynamics to realize our resilient and efficient perception and behavior. In this paper, by considering similarities and differences of the brain and information networks, we discuss a possibility of information networks with brainlike continuing internal dynamics. We expect that the proposed networks efficiently realize context-dependent in-network processing. By introducing recent findings of neuroscience about dynamics of the brain, we argue validity and clues for implementation of the proposal.

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A bio‐inspired OSPF path selection scheme based on an adaptive attractor selection model

Gong

Yang

Zhang

et al. 2015

Int J Communication

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Current Internet Protocol routers only support equal cost multi-path routing, which performs the random path selection or the traffic uniform distribution among equal-cost paths. In biology, an adaptive attractor selection model is presented to simulate the concentration changes of two kinds of Escherichia coli's mRNA in changing nutrition environments with bistability equations. Inspired by the metabolism behaviors of E. coli, we propose an adaptive path selection scheme Open Shortest Path First-path selection by attractor selection to dynamically select the transmission path by the real-time path quality. Here, the mRNA concentration is analogous to the path quality. Then, to reflect the multipath quality, multi-stability equations are adopted and redesigned. Our scheme consists of two main features. The first one is a redefined path-activity to indicate multipath transmission goodness, which is inversely proportional to the offset between current path quality and best path quality. And the second one is a new attractor expression of the multi-stability equations to concretely specify the effect of a stochastic item noise in the equations on the path selection. Compared with the greedy selection and the uniform random selection in file transfer protocol (FTP) service, our scheme gains better performance on reducing file transmission time, traffic throughput, and traffic dropped.the adaptive attractor selection model [8] is introduced into the field of the computer networks to optimize the multipath traffic transmission.Originally, the attractor selection model [9] is proposed to simulate the metabolism behaviors of the Escherichia coli in the dynamic nutrition environments with the revised bistability equations. In nature, each individual E. coli always regulates adaptively its own state in the different nutrition environments. When nutrition is adequate, the metabolism of the E. coli becomes vigorous generally. Then, the mRNA concentrations in the E. coli greatly increase to produce enough nutrition materials for growth and reproduction. Furthermore, the vigorous state will be kept if nutrition is adequate. Subsequently, when nutrition is inadequate, the metabolism of the E. coli is weak. Then, the mRNA concentrations inevitably decrease. Moreover, the concentrations are able to change stochastically to make the E. coli shift to a suitable metabolism state in the current nutrition environment [9]. Mathematically, this adaptation process of the E. coli to the changing nutrition environments can be modeled by the bistability equations, in which each attractor formulates a stable adaptation state of the mRNA concentrations.There are two key parameters in the bistability equations. One is called activity, which indicates the adaptation goodness of the E. coli to the nutrition environment. And the other one is noise, which takes the stochastic effect on the mRNA concentrations. The two parameters have a relation of ebb and flow. When the activity increases, the stochastic effect of the noise on the mRNA concentrati...

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Resilient Multi-path Routing Based on a Biological Attractor Selection Scheme

Cited by 20 publications

References 18 publications

A Self-Adaptive Routing Protocol in Wireless LANs Based on Attractor Selection

A Self-Adaptive Routing Protocol in Wireless LANs Based on Attractor Selection

Brain-Inspired Communication Technologies: Information Networks with Continuing Internal Dynamics and Fluctuation

A bio‐inspired OSPF path selection scheme based on an adaptive attractor selection model

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